PROJECT SUMMARY The Strong Heart Study (SHS) is a multi-center, longitudinal resource designed to better understand cardiovascular disease in American Indians, identify significant risk factors, promote new research and deliver better health care. To achieve these goals, SHS data should be accessible to interested and qualified researchers, while no harm is done to the study participants who contribute their data. Thus private information in the data and the identity of the participants should be protected, and SHS tribal sovereignty and agreements that include tribal review and approval of all SHS data use requests should be respected. Our study aims to address these issues using advanced technologies and scientific computing toolkits to enable shared, but protected, data access, as well as to understand the data sharing preferences of SHS participants. The first aim is to develop an innovative, secure data-centric service to protect computation on SHS data according to governance practices that are acceptable to participating SHS tribes, SHS investigators, and the NIH. Specifically, we will build a system for secure analysis on protected data through a virtual private network, in which only approved operations and outputs are permitted. The proposed framework will allow researchers to easily and securely perform specific statistical analysis on SHS data and meta-analyses. The second aim is to develop novel federated computing models to support the SHS Coordinating Center and Genetics Center to analyze data in a distributed manner. The methods for achieving the second aim rely on new, practical federated data analysis technology. For example, in the case of vertically partitioned data, different data from the same SHS participants may be stored at different sites, such as genomic data and phenotype data that are currently stored at the SHS Genetics Center and the SHS Coordinating Center, respectively. The third aim is to understand the data sharing expectations and preferences of SHS participants to inform the implementation of the data sharing models. This aim will be carried out through qualitative and quantitative methods, which include the use of individual interviews and surveys of SHS participants.